Machine Learning Engineer

Deploy•San Francisco, CA

About The Position

Deploy is an intelligence infrastructure platform providing developers and enterprises access to open source and proprietary AI models. Its offerings include an optimized inference engine for leading open source large language models and proprietary models for content safety, AI and deepfake detection, sponsorship intelligence, and intellectual property and publicity protection. The company has more than 250 employees across offices in San Francisco, Seattle, New York, and Delhi, and has raised more than $200 million from investors. Deploy is seeking machine learning engineers to prototype state-of-the-art neural network models and bring them into production. The role involves applying machine learning models to production use cases, from model design and data preparation through training, deployment, and ongoing performance improvement. Candidates should be comfortable working with large-scale datasets, learning technologies across the machine learning stack, taking ownership, and developing scalable, maintainable data pipelines. The company describes itself as an ambitious team building an AI company. The role offers a steep learning curve and the opportunity to contribute directly to the company's development. The expected base salary range is $120,000–$180,000; stock options may also be offered. Compensation may vary based on qualifications, skills, competencies, experience, and location. AI tools may be used to assist with parts of the hiring process, while final hiring decisions are made by humans.

Requirements

Undergraduate or graduate degree in computer science or a similar technical field, with significant coursework in mathematics or statistics.
1–2 years of industry machine learning experience.
Successfully trained and deployed a deep learning model in image, NLP, video, or audio into production, with measurably improved performance over baseline; experience may come from industry or a personal project.
Strong experience with a high-level machine learning framework such as TensorFlow, Caffe, or Torch, and familiarity with the others.
Strong Python skills, especially for machine learning frameworks.
Ability to quickly code and prototype data pipelines using combinations of Python, Node, bash, and Linux command-line tools, especially for large datasets consisting of millions of files.
Working knowledge of C++, Scala/Spark, SQL, Cassandra, and Docker, or willingness to learn them quickly.
Up-to-date understanding of deep neural network research and architectures, including the theory and motivations behind techniques and how to implement them in a machine learning framework.
Strong communication skills and ability to work with others.
Strong teamwork and a do-whatever-it-takes attitude.

Responsibilities

Design and implement neural networks for production use cases.
Gather and refine data, then train and tune machine learning models.
Deploy models at scale with high throughput and uptime.
Analyze model results in production and continuously improve accuracy and speed.
Work closely with Backend and DevOps teams and internal data-labeling services.
Use OWASP Top 10 techniques to secure code from vulnerabilities.
Maintain awareness of industry best practices for data maintenance and handling as they relate to the role.
Follow policies, guidelines, and procedures for protecting information assets.
Report actual or suspected security or policy violations and breaches to an appropriate authority.